nanogpt

Train a minimal GPT implementation with PyTorch causal self-attention.

Updated May 4, 2026

Search Tags:#training #pytorch #gpt #tokenization #distributed-training #transformers #language-modeling

Ready to run

npx skills add https://github.com/Supporter09/Face_Anti_Spoofing_Biometric --skill nanogpt-supporter09

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: nanogpt
Source: https://github.com/Supporter09/Face_Anti_Spoofing_Biometric/tree/main/.claude/skills/nanogpt
Command: npx skills add https://github.com/Supporter09/Face_Anti_Spoofing_Biometric --skill nanogpt-supporter09

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you understand, train, and sample from a minimal GPT implementation so you can learn transformer language modeling without relying on a large framework or opaque abstractions.

Core Features & Use Cases

Minimal GPT Architecture: Explore a compact, hackable transformer with causal self-attention, residual blocks, tied embeddings, and autoregressive generation.
End-to-End Training Workflow: Follow reusable steps for data preparation, batching, optimization, mixed precision, checkpointing, evaluation, and text sampling.
Use Cases: Train a tiny Shakespeare model on CPU, reproduce GPT-2 style training on OpenWebText with multiple GPUs, fine-tune pretrained checkpoints, or adapt the pipeline to your own text dataset.

Quick Start

Use the nanogpt skill to guide you through training a small Shakespeare model and generating sample text.

Frequently Asked Questions about nanogpt

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

How do I train a GPT model from scratch using PyTorch?▼

Training a GPT model from scratch involves building a minimal transformer with causal self-attention and autoregressive generation in PyTorch. You prepare binary tokenized datasets, configure optimization with cosine learning-rate warmup, and execute mixed-precision training to learn language modeling.

Can I reproduce GPT-2 training on OpenWebText with multiple GPUs?▼

Yes, you can reproduce GPT-2 style training on OpenWebText using multiple GPUs. The workflow supports distributed data parallel execution, allowing you to scale training across GPUs while applying gradient accumulation and mixed-precision optimization.

What's the best way to fine-tune pretrained GPT checkpoints on a custom text dataset?▼

The best way to fine-tune pretrained GPT checkpoints is to adapt the reusable training pipeline to your custom text dataset. You prepare your text data into a binary tokenized format, then apply the existing optimization and mixed-precision training steps to update the model weights.

Do I need distributed data parallel execution to train a character-level Shakespeare model?▼

No, you do not need distributed data parallel execution to train a character-level Shakespeare model. The minimal GPT implementation is compact enough to run character-level training experiments on a CPU or a single GPU without distributed setups.

How does mixed-precision training and gradient accumulation work in a minimal transformer implementation?▼

Mixed-precision training and gradient accumulation optimize memory and throughput in a minimal transformer implementation. Mixed precision uses lower-precision tensors during forward passes, while gradient accumulation simulates larger batch sizes by accumulating gradients over multiple steps before updating weights.

Why use a minimal GPT implementation instead of a large transformer framework for language modeling?▼

A minimal GPT implementation provides a hackable architecture with tied embeddings and residual blocks to help you understand transformer language modeling. It avoids the opaque abstractions of large frameworks, making it ideal for learning the mechanics of causal self-attention and end-to-end training workflows.